R Notebook

test <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) 

gg_color_hue(4)

[1] "#F8766D" "#7CAE00" "#00BFC4" "#C77CFF"

calculates relative y value based on data from feat.mat

calculate_yrel <- function(theta,dist){ yrel=dist*cos(theta) return(yrel) }

function calculates xrel based on track.mat file

in the case of the male, right and left wing angles were swapped, so in

that case, the names of the arguments to the functions don’t make sense.

This is due to ‘ipsi’ and ‘contra’ having a different meaning when male

position relative to female is examined.

determining whether ipsi or contralateral wing is extended.

calculate_xrel <- function(ori,xmale,xfemale,right_angle,left_angle,yrel,ppm){ xmale_mm=xmale/ppm; xfemale_mm=xfemale/ppm;

if (abs(right_angle)>abs(left_angle)) { xrel = (xfemale_mm - (cos(ori)yrel+xmale_mm))/(sin(ori)) } else { xrel = -(xfemale_mm - (cos(ori)yrel+xmale_mm))/(sin(ori)) }

if (xrel >20 | xrel<-20) { xrel=NA } }

test$dist_to_other__mm[1]

[1] 17.92103

test$dist_to_other__mm[2]

[1] 17.92103

test$facing_angle__rad[1]

[1] 1.410952

test$facing_angle__rad[2]

[1] 1.208154

test <- test %>%
  mutate(rel_x = ,
         rel_y = )

unique(new_data$FileName)

[1] "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3"  "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2"

new_data <- new_data %>% 
  filter(FileName!="Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2")
unique(new_data$FileName)

[1] "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3"

new_data %>% 
  filter(genotype!="CS female") %>% 
  filter(rel_y_other > -20 & rel_y_other < 20) %>% 
  filter(rel_x_other > -20 & rel_x_other < 20) %>% 
  ggplot(aes(x=rel_x_other,y=rel_y_other)) +
  geom_point() +
  coord_fixed()

new_data %>% 
  filter(genotype!="CS female") %>%
  filter(dist_to_other__mm > 2) %>% 
  filter(rel_y_other > -20 & rel_y_other < 20) %>% 
  filter(rel_x_other > -20 & rel_x_other < 20) %>% 
  ggplot(aes(x=rel_x_other,y=rel_y_other)) +
  geom_bin2d(bins = c(100,100)) +
  # xlim(0,2*pi) +
  # ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(10,1000),na.value = "white") +
  # theme_void() +
  coord_fixed()

new_data %>% 
  filter(genotype!="CS female") %>%
  filter(dist_to_other__mm > 2) %>% 
  #filter(rel_y_other > -20 & rel_y_other < 20) %>% 
  #filter(rel_x_other > -20 & rel_x_other < 20) %>% 
  ggplot(aes(x=rel_x_other,y=rel_y_other)) +
  geom_bin2d(bins = c(100,100)) +
  # xlim(0,2*pi) +
  # ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") #+

  # theme_void() +
  #coord_fixed()

new_data %>% 
  filter(genotype!="CS female") %>%
  filter(dist_to_other__mm > 2) %>% 
  filter(rel_x_other < -20 | rel_x_other > 20) %>% 
  select(FileName,Arena,Id,Frame,dist_to_other__mm,facing_angle__rad,ori__rad,pos_x__px,pos_y__px,rel_x_other,rel_y_other)

(xfemale_mm - (cos(ori)*yrel+xmale_mm))/(sin(ori))

new_data %>% 
  filter(genotype!="CS female") %>%
  filter(dist_to_other__mm > 2) %>% 
  filter(rel_x_other < -20 | rel_x_other > 20) %>% 
  select(Arena,Id,Frame,dist_to_other__mm,facing_angle__rad,ori__rad,pos_x__px,pos_y__px,rel_x_other,rel_y_other) %>% 
  mutate(cos_ori = cos(ori__rad),
         sin_ori = sin(ori__rad))

p1 <- new_data %>% 
  filter(genotype!="CS female") %>%
  filter(dist_to_other__mm > 2) %>% 
  filter(rel_x_other < -20 | rel_x_other > 20) %>% 
  ggplot(aes(x=FileName,y=ori__rad))+geom_point()+ylim(-3.3,-2.8)
p2 <- new_data %>% 
  filter(genotype!="CS female") %>%
  filter(dist_to_other__mm > 2) %>% 
  filter(rel_x_other < -20 | rel_x_other > 20) %>% 
  ggplot(aes(x=FileName,y=ori__rad))+geom_point()+ylim(-0.6,0.6)
p3 <- new_data %>% 
  filter(genotype!="CS female") %>%
  filter(dist_to_other__mm > 2) %>% 
  filter(rel_x_other < -20 | rel_x_other > 20) %>% 
  ggplot(aes(x=FileName,y=ori__rad))+geom_point()+ylim(2.8,3.2)
ggarrange(plotlist = list(p1,p2,p3),
          ncol = 3,
          nrow = 1)

Removed 653 rows containing missing values (geom_point).Removed 442 rows containing missing values (geom_point).Removed 621 rows containing missing values (geom_point).

new_data %>% 
  filter(genotype!="CS female") %>%
  filter(dist_to_other__mm > 2) %>% 
  filter(rel_y_other < -20 | rel_y_other > 20)

temp2 %>% 
  filter(genotype!="CS female") %>%
  filter(dist_to_other__mm > 2) %>% 
  #filter(rel_y_other > -20 & rel_y_other < 20) %>% 
  #filter(rel_x_other > -20 & rel_x_other < 20) %>% 
  ggplot(aes(x=rel_x_other,y=rel_y_other)) +
  geom_bin2d(bins = c(100,100)) +
  # xlim(0,2*pi) +
  # ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") #+

  # theme_void() +
  #coord_fixed()

Proportion time in sector and within 10mm

There were 40 warnings (use warnings() to see them)

Proportion time in sector and within 10mm

Warning messages:
1: Unknown or uninitialised column: 'dist_to_other__mm'. 
2: Unknown or uninitialised column: 'dist_to_other__mm'. 
3: Unknown or uninitialised column: 'dist_to_other__mm'. 
4: Unknown or uninitialised column: 'dist_to_other__mm'. 
5: Unknown or uninitialised column: 'facing_angle__rad'. 
6: Unknown or uninitialised column: 'facing_angle__rad'. 
7: Unknown or uninitialised column: 'facing_angle__rad'. 
8: Unknown or uninitialised column: 'facing_angle__rad'. 

There were 24 warnings (use warnings() to see them)

colour=c(ifelse(rollavg_dist_to_other>2.5,gg_color_hue(2)[2],gg_color_hue(2)[1]))

There were 24 warnings (use warnings() to see them)

There were 16 warnings (use warnings() to see them)